For query expansion through (pseudo-) relevance feedback, we use pseudorelevance feedback based on high-scoring sub-documents to augment the queries.

after retrieving some sub-documents for a given topic from the target corpus, we take a set of top ranked sub-documents, regarding them as relevant sub-documents to the query, and extract terms from these sub-documents.


We If blob-token i b satisfies the context of feature constraints and keyword "water" also occurs in image I.

if the color and texture feature components are coordinated with the semantic label 'water', and then the value of the feature function is 1, otherwise 0.


In the recent past, many models for automatic image annotation are limited by the scope of the representation.

they fail to exploit the context in the images and words.


While such POS-retraining is a direct remedy to learning appropriate tags for new vocabulary, it is only a partial fix to a larger problem.

the trees found in PTB codify a relationship between PTB POS tags and constituent structure, and any mismatch between the tagging schemata used in PTB and that used by our new corpus could result in misapplication or underutilization of the bracketing rules acquired by the parser during training.


Our interpretation of this latter result is that the primary limitation of the lexicon is coverage, despite its size.

when either of the other adaptations were used, the lexicon did not offer much beyond them.


The latter VP in coordinated VPs is often parsed as an IP due to pro-drop by the baseline parser.

the coordinated structure VP is parsed as: V P 0 → V P 1 IP 2 .


Most of such morphemes are endings of verbs or adjectives, or particles.

the rightmost characters in a bunsetsu are expected to indicate the syntactic role of a bunsetsu.


For example, the frequency count of "mode" does not include that of "model", because it is indexed separately.

search engines use this indexing strategy at the string level for languages in which words are separated by spaces, and in our case, we need a search engine in which the count of x n includes that of x n+1 .


For example, the attributes of car objects will be weight, engine, steering wheel, driving feel, and manufacturer.

attributes are items whose values we want to know when we want to know about the object.


In turn, this reduces the chances of any two ngrams to align, since ngram alignment requires the complete matching of the corresponding anchors.

the amount of information captured in the anchors is a trade-off between coverage (when anchors are less specific) and accuracy of the acquired paraphrases (when the anchors are more specific).


The aggregated evaluation results, shown in bold in Table 3, suggest that Ngram-Only leads to paraphrases of lower quality than those extracted with Ngram-Entity and Ngram-Relative.

the samples from the middle and bottom of the Ngram-Only paraphrases contain a much higher percentage of incorrect pairs.


Then, we examined whether a user can easily choose and access information for solving problems.

we examined whether there was good connection between the significant sentences or not, and the confirmation label was proper or not.


Note that applying a partial parsing rule results in a structure of depth one or more.

the rules extracted by this rule template reduce a substring into a subtree, as opposed to a single non-terminal; hence, the resultant rules can be applied more specifically and strictly.


The precision of the "underspecified" grammar was above 95%.

when a parser generates 20 structures, 19 out of 20 structures are correct.


In the future, we will study more effective way to resolve structure ambiguities.

consider the tradeoff effect between grammar coverage and precision.


However, we cannot simply estimate this model, because of the problem that has been discussed in studies of the probabilistic modeling of unification-based grammars [1,2].

the exponential explosion of parse candidates assigned by the grammar makes the estimation intractable.


Since Enju represented a probabilistic disambiguation model in a packed forest structure, we exploit that structure to represent our probabilistic model.

we redefine p M with feature functions g j on conjunctive nodes as  As described in Section 2, the original model, p E (t|s), is expressed in a packed structure as where Then, p 0 (t|s) is substituted by p E (t|s), and p M (t|s) is formulated as With this form of p M (t|s), a dynamic programing algorithm can be applied.


However, not all of them are consistently used as sentence ends.


To utilize such  lexical resource, a multi-valued feature isAbbr is introduced to the feature set to indicate whether a target word is in the abbreviation list and what it is.

all words in the list actually play a role equivalent to individual bi-valued features, under the umbrella of this new feature.


The constraint C1 means that the word in the outside part of the other text is considered as a correspondent word, and a captured word is unlikely to be corresponding.

appearance of the word itself is more reliable than local context coincidence.


The experiment on comparable news data demonstrated the robustness of our method by attaining a precision of 76.0% for English data and 76.3% for Japanese data.

prevention of outside-appearance played an important role by improving the precision greatly.


The problem is to search the most probable class assignment for each entity and each relation of interest, given the observations of all entities and relations.

the problem is to solve the following two equations, using two kinds of constraint knowledge ε α α , R and the interaction among entities and relations.


But while loose translations by themselves already have numerous applications, truly parallel sentence translations provide invaluable types of information for the aforementioned types of mining and induction, which cannot easily be obtained from merely loose translations or comparable sentence pairs.

truly parallel bi-sentences are especially useful for extracting more precise syntactic and semantic relations within word sequences.


The ITG Hypothesis posits a strong language universal constraint that can act as a strong inductive bias for various language learning problems, resulting in both efficiency and accuracy gains.

the hypothesis asserts that sentence translation between any two natural languages can be accomplished within ITG expressiveness (subject to certain conditions).


Thus, we assess the contribution of ITG ranking by computing standard uninterpolated average precision scores used to evaluate the effectiveness of ranking methods.

in this case, this is the expected value of precision over the rank positions of the correctly identified truly parallel bi-sentences: where T is the set of correctly identified bi-sentences.


It is clear that the smaller the average within-class distance or the average between-class distance, the larger the within-segment word similarity or the between-segment word similarity, and vice versa.

we want to find the best segmentation of a document in which within-segment word similarity is the largest, and between-segment word similarity is the smallest.


In this experiment, we assume the number of segments of a document is unknown in advance.

texttiling algorithm and our model should determine the number of segments of a document automatically.


This paper also discusses basic techniques when applying PLSI to the automatic acquisition of synonyms.

the following are discussed from methodological and experimental views: (1) choice of distance/similarity measures between probability distributions; (2) filtering words according to their frequencies of occurrence; and (3) multiple executions of PLSI and integration of the results.


The last word of a baseVP represents the entire baseVP to which it belongs.

all the dependencies directed to words in a baseVP are redirected to the last verb of the baseVP.


The corresponding vector n i to noun n i is given by: -tf•idf: The vectors given by tf method are weighted by idf.

where idf j is given by using df j , the number of distinct nouns that co-occur with verb v j .


The proposed algorithm works in the top-down manner.

the algorithm attempts to find the most appropriate concept from the top level, and it will move down if the lower concepts yield better scores.


The third example shown in Table 4 illustrates the case that is considered to be subset mapping.

the EDR concept selected by the algorithm is subconcept of the MMT concept.


Finally, since the size of the input parse tree is not constant, the similarity score is normalized as follows: The value of 1 2 ( , ) K T T ranges from 0 to 1.

the above similarity score is more than one.


This paper studies the problem of mining relational data hidden in natural language text.

it approaches the relation classification problem with the strategy of transductive learning.


This study focuses on the second subproblem, RDC.

the goal is to automatically classify binary relations between entities, i.e., to decide in which relational table to put each entity pair, using transductive learning algorithms.


On the other hand, we might encounter the following situation: we are only concerned about performance on a particular pool of data, -and we don't care about generalizability, -and data points can be effectively queried/accessed If all the conditions above are true, the learner can observe the test data and potentially exploit structures in their distribution.

there is really no difference between "unlabeled data" and "test data", and the research question is: "given some labeled data and a large set of (unlabeled) test data, can properties of the entire data set be used to make predictions?"


The research problem of this paper is classification of relations between entities.

the task is to determine the appropriate relational table into which one should put a given pair of related entities.


the (two) EDT mentions of the entity arguments of a relation must occur within a common syntactic construction, in this case a sentence.


aCE-2 version 1.0 is used, which contains 519 files from sources including broadcast, newswire, and newspaper.


This paper approaches the relation classification problem with improved transductive learning.

we learned the following: -Application of transductive learning on NLP problems, including information extraction, has been under-explored.


As the first stage, we propose a method for classifying multiple-sentence questions into question types.

we first extract the core sentence from a given question text.


Therefore, as a first step towards developing a QA system that can handle multiple-sentence questions, we propose a method for classifying multiplesentence questions.

in this work, we treat only questions which require one answer.


[6] enriched English with knowledge to help select the correct fullform from morphologically richer languages such as Spanish and Catalan.

they introduced a splicing operation that merged the pronouns/modals and verbs for treating differences in verbal expressions.


In addition, [6] and [8] proposed re-ordering operations to make similar word orders in the source and target language sentences.

for the interrogative phrases with different word order from the declarative sentences, they introduced techniques of question inversion and removed unnecessary auxiliary verbs.


When the target language was Korean, we had higher BLEU scores in all the morpho-syntactic models but lower NIST scores.

we took advantage of generating more accurate full-form eojeol with positional information, i.e.


Rather, the structure is given by whatever structure the kernel and the change operators construct during generation.

the structure our approach uses is (1) restricted to the question to be generated and does not apply to the document level, and (2) in tradition with the lazy learning characteristics of memory-based approaches is generated on the fly on an as-needed basis, as opposed to being dictated from the outset at design time.


An individual definition question will be scored using nugget recall (R) and an approximation to nugget precision (P) based on length.

r = # essential nuggets returned in response/# essential nuggets P is defined as: if length < allowance, P = 1 else P=1-[(length-allowance)/length] where allowance = 100*(# essential+acceptable nuggets returned) length = total # non-white-space characters in answer strings The F measure is: (2) where β value is fixed three in TrEC 2004, and we also use three to get comparable result.


Our goal is to learn the best transforms for the question pattern "how old."

we want to find out what terms are associated with "how old" in the answer passages.


In addition, Table 8 also shows that less user's efforts are needed by using our approach.

for each question, the average of summaries required to be viewed by human beings goes down from 2.7 to 2.3.


The second term in Equation 1is the pair-wise mutual information (PMI) between n S 1 and n O 1 .

in order to simplify the computation of this term, we assume a pair-wise mutual information independence (2): an individual state is only dependent on the observation sequence n O 1 and independent on other states in the state sequence n S 1 .


All of our experiments are evaluated on the PK and CTB benchmark corpora used in the First SIGHAN Chinese word segmentation bakeoff 7 with the closed configuration.

only the training data from the particular corpus is used during training.


Given a segmentation result, we assume that unidentified new words may be a sequence of unattached characters.

all multiple-character words in the given result are considered correct, while single words, which might include unidentified new words will be rechecked by the chr-HMM.


A sequence of words which for one reason or another is more sensibly treated as a single lexical item, rather than as individual words, is known as a multiword expression (MWE).

an MWE is a sequence of words which together behave as though they were a single word.


Two words whose probability of appearing together is greater than that which would be expected based on their individual frequencies, are considered to constitute a potential MWE and are extracted for later processing.


We plan to evaluate the performance of the BNC models described above on another corpus to determine their flexibility.

we plan to use a corpus of North American English such as the Penn Treebank, in the hope of demonstrating the models' ability to handle American as well as British English.


According to Hong [6], the rate of subject drop is 57% in spoken Korean, which is higher than other elements.

when the subject refers to a nominal entity mentioned in the previous utterance, it naturally disappears in speech rather than ap-pearing as a pronoun.


However, the current annotations of PKT and ST treat topic marker nun as the same auxiliary postposition, which is similar to other postpositions man 'only', to 'also', and mace 'even'.

pKT and ST represent a subject Np with a topic marker as the subject, while an object with a topic marker is treated as a scrambled argument out of its canonical position.


For example, the first four PCs can justify more than 90% (91.28%) of the total sample variance, we can choose them without much loss information.

sample variance is summarized very well by these four PCs and the data from 9 observations on 9 variables can be reasonably reduced to 9 observations on 4 PCs.


Thus, automatic knowledge discovery and efficient information access are strongly demanded to curate domain databases, to find out relevant information, and to integrate/update new information across an increasingly large body of scientific articles.

since most biomedical texts introduce specific notations, acronyms, and innovative names to represent new concepts, relations, processes, functions, locations, and events, automatic extraction of biomedical terminologies and mining of their diverse usage are major challenges in biomedical information processing system.


In the ME framework, the conditional probability of predicting an outcome o given a history h is defined as follows: where is a binary-valued feature function, λ i is the weighting parameter of f i (h, o), k is the number of features, and Z λ (h) is a normalization factor for Σ o p λ (o|h)=1.

the probability p λ (o|h) is calculated by the weighted sum of active features.


We attempt to reduce a word to its stem or root form with a simple algorithm which has rules for words containing plural, hyphen, and alphanumeric letters.


For this, we add two special features : previous state (label) and conjunction of previous label and current word to consider state transition.

a previous label can be represented as a feature function in our model as follows: It means that the target word is likely to be inside a term (I), when the word is "gene" and the previous label is "B".


Since our system considers all NEs belonging to other classes in the recognition phase, it tends to recognize the longest ones.

in the term classification phase, such NEs are classified as "other" class and are ignored.


We first retrieved all NEs including embedded NEs and longest NEs from GENIA 3.02 corpus and change  the outputs of all other classes except the target 5 classes to O.

the input of FST is a sequence of words in a sentence and the output is categories corresponding to the words.


In the shared task, only biomedical named entities which belong to 5 specific classes are annotated in the given training data.

terms belonging to other classes in GENIA are excluded from the recognition target.


Also, Table 6 shows the results of semantic classification.

the system often confuses protein with DNA, and cell line with cell type.


We proposed two exponential models for each phase.

cRFs are used for term detection phase including Markov process and ME is used for semantic labeling.


This frequency for each possible type of the entity X is regarded as sense order information.

we can assign to each possible type a sense number in the descending order of the frequency.


[1] says that we can consult a commonly-used acronym to determine extent of a named entity.

"University of California, Los Angeles", for example, must be annotated as a unique organization name since the university is commonly referred to as 'UCLA'.


On the contrary, just 47 instances were incorrectly matched when we tried to search any acronyms of super-phrases of each key instance.

acronym can detect potential inclusion at 46.58 applicability and 96.05 accuracy.


A dictionary contains only basic compounds, but there are many more Katakana compounds in real texts.

the direct use of dictionary is not enough to handle real Katakana compounds.


If no candidate has a positive record in the end, we deem the NP in question as non-anaphoric and leave it unresolved.

a NP would be resolved to a candidate only if the candidate won at least one competitor with confidence above the threshold.


Thus this situation has been one of the main causes for errors occurred in speech acts analysis.

the sparse data problem from low frequency of some speech acts has commonly occurred in the previous research [8].


In a slide, topic/non-topic parts that are extracted from the original text are itemized and each item is indented based on the discourse structure of the text.

a big contrast/list structure in the text is an important clue for producing an easy-to-read slide.


Converting original texts into multimodal presentation (summary slides and speech synthesis) is significantly better in comparison with presenting original texts, even if there are some errors in generated slides.

a slide in which a contrast/list structure is detected is far easier to read than an original text, as shown in Figure 9.


As the method operates exclusively on pseudo glosses generated on the basis of the hypernymy relation, this type of information from definitions turns out to be the most important one for computing semantic relatedness.

definitio per genus proximum is superior to definitio per differentia specifica in this task.


Given a triple <x i , r, x j >, we identify the set Ψ r full of all paths ψ i,j such that ψ i,j indicates r i,j and ψ i,j 's sequence of relations {R i,i+1 .. R j-1,j } matches one of the allowed sequences.


In response to the questions raised in the introduction, firstly, the results for the known constituent condition (F 1 of 90.56 and 89.07 for textbook data and news data respectively) have shown that even if we do not use parse-dependent features such as governing category and parse tree path, results are not particularly affected.

lexical features are already very useful as long as the constituent boundaries are given.


Thus, it is favorable for error detecting systems aiming at Japanese learners of English to be capable of detecting article errors.

such systems need to somehow distinguish mass and count nouns in the writing of Japanese learners of English.


According to Chen & Lee (2004)'s study [1] of Academia Sinica Balanced Corpus (abbreviated ASBC corpus hereafter), about 7% of named entities are titles of creations.

more than one-third of rarely mentioned named entities are titles of creations.


most titles might look just like common strings in sentences.


These unmarked titles usually stand for widely known or classic creations.

these famous works are supposed to be mentioned in many documents many times.


To exploit contextual features, our system adopts a word-based, positionfree unigram context model with a window size of 5.

our context model can be viewed as a combination of ten different contextual features of the naïve Bayesian classifier, five of them are left context and the other five are right context.


In addition, we contrast the effectiveness of priors with an alternative, parameter-free approach.

we employ logarithmic opinion pools (LOPs).


In this paper we investigate other possibilities.

we allow the means to take on non-zero values, and the variances to be featuredependent.


In this paper we also consider a parameter-free method.

we explore the use of logarithmic opinion pools [3].


First, several studies [9,3,19] have shown that the theory of the LCS provides a systematic explanation of semantic decomposition as well as syntax determines.

kageyama [9] has shown that even a simple typology of LCS can explain a wide variety of linguistic phenomena including word association within compounds, transitivity alternation, and lexical derivation.


We thus prioritize the rightmost predicates in this matching process.

the proceeds from the rightmost inner predicates to the outer ones, and the matching process is repeated until the leftmost predicate in LCSN0 or that in LCSV 1 matched.


If the leftmost argument of LCSS has the same value as either z or y in LCSN1, the model makes the argument a subject (nominative).

the passive voice is selected and case alternation (passivization) is applied.


Although the performance has room for further improvement, we think the performance is reasonably high under the current stage of the T-LCS dictionary.

the tendency of errors does not so differ from our expectation.


The reason is because most of WSD studies have focused on only a small number of ambiguous words based on sense tagged corpus.

the previous WSD systems disambiguate senses of just a few words, and hence are not helpful for other NLP applications because of its low coverage.


[9] followed the method of [8], but tried to resolve the ambiguous relative problem by using just unambiguous relatives.

the ambiguous relative rail is not utilized to build a training data of the word crane because the word rail is ambiguous.


2 A possible alternative is to use the unambiguous relatives in the long distance from a target word, but the way is still problematic because the longer the distance of two senses is, the weaker the relationship between them is.

the unambiguous relatives in the long distance may provide irrelevant examples for WSD like ambiguous relatives.


One reason of the low performance is on the relatives irrelevant to the target words.

investigation of several instances which assign to incorrect senses shows that relatives irrelevant to the target words are often selected as the most probable relatives.


How accurate and how robust can such a system be?

we are interested in the following three questions: How much advantage can we gain from the rich-feature approach by careful extraction and treatment of the rich features?


Note that although milk in the input NC also occurs in a training exemplar, the semantic relations for the individual NCs differ.

while apple juice is juice made from apples (MATERIAL), morning milk is milk served in the morning (TIME).


In WordNet::Similarity, relatedness goes beyond concepts being similar to each other.

wordNet provides additional (non-hierarchical) relations such as has-part and made-of.


We compare the performance of these methods from various angles by adapting the baseline model to four adaptation domains.

we attempt to interpret the results given in terms of the character error rate (CER) by correlating them with the characteristics of the adaptation domain measured using the information-theoretic notion of cross entropy.


This paper presents an empirical study of several LM adaptation methods on the task of Japanese text input.

we focus on the so-called cross-domain LM adaptation paradigm, i.e.


Given the two distributions p and q above, the KL divergence is defined as The cross entropy and the KL divergence are related notions.

given the notations of L, p and q above, [5] shows that the cross entropy takes into account both the similarity between two distributions (given by KL divergence) and the entropy of the corpus in question, both of which contribute to the complexity of a LM task.


In evaluating both MAP and discriminative methods, we used an N-best rescoring approach.

we created N best hypotheses using the background trigram model (N=100 in our experiments) for each sentence in test data, and used domain-adapted models to rescore the lattice.


In Table 3, we see that for all sizes of training data for all adaptation methods, the following order of CER performance is observed, from better to worse: Yomiuri TuneUp Encarta Shincho.

the more similar the adaptation domain is to the background domain, the better the CER results are.


Figure 3 displays the self entropy of four adaptation corpora along the X-axis, and the improvement in CER reduction when 72K-sentence adaptation data is used over when 800 sentences are used along the Yaxis.

for each adaptation method, each point in the figure corresponds to the CER reduction ratio on a domain (corresponding to Yomiuri, Encarta, TuneUp, Shincho from left to right) when 90 times more adaptation data was available.


In this paper, we compared several different language models on two classification tasks: book recognition and authorship attribution.

we evaluated syntactic elements of expression consisting of sentence-initial and -final phrase structures, semantic and syntactic categories of verbs, and linguistic complexity measures, on recognizing books (even when they are derived from the same title) and on recognizing authors.


For a similar reason as to FSR, the logarithm of AV is used as goodness measure, and only substrings with AV > 1 are considered word candidates.

we have g AV (w) = logAV (w) for a word candidate w. Boundary Entropy (Branching Entropy, BE) It is proposed as a criterion for unsupervised segmentation in some existing works (Tung and Lee, 1994;Chang and Su, 1997;Huang and Powers, 2003;Jin and Tanaka-Ishii, 2006).


5 With this assumption in mind, Eq (5) reduces to P (u|t)P (t).

with independence assumptions and a local time horizon, we estimate: we make the assumption that a sequence of underlying morphemes and tags corresponds to just one word.


The current trend is to explore kernel-based methods (Haussler, 1999) which can implicitly explore features in a high dimensional space by employing a kernel to calculate the similarity between two objects directly.

the kernel-based methods could be very effective at reducing the burden of feature engineering for structured objects in NLP, e.g.


The first is that the sub-trees enumerated in the tree kernel are context-free.

each sub-tree enumerated in the tree kernel does not consider the context information outside the sub-tree.


The reason why we choose SVM is that it represents the state-of-theart in machine learning research and there are good implementations of the algorithm available.

sVMLight (Joachims 1998) is selected as our classifier.


This variation is much more when the IL words are written using the Latin script ( Table-1).

the amount of ambiguity is very high when we try to build a system that can be used for purposes like designing input mechanisms, instead of just for transliteration of NEs etc.


Gap resolution matches between memorized antecedents and induced gaps to associate ellipses to their antecedents during derivation of coordination and serialization.

two syntactic categories 2 D 1 F 1 C and 3 D 2 F 2 C are matched up and canceled from the resulted syntactic category, if they have the same syntactic categories C, their directions D 1 and D 2 are equal, and their memorized/induced formulae F 1 and F 2 are unified.


assigning the apriori probability of collocational strength without empirical evidence is both subjective and difficult.


On the other hand, P (e 1 |Ū ) captures the probability of s occurring in X ∩ Y not as a unit.

p (e 1 |Ū ) is the probability of s not occurring in X ∩ Y , or equivalently, equal to p ((X ∩ Y \ S)|(X ∩ Y )).


The general trend that we see for all five dimensions of linguistic quality is that the more the text is automatically altered, the worse the linguistic quality of the summary gets.

the grammaticality of the summaries drops significantly for the rewrite systems.


We now examine the question of how the content in the summaries changed due to the NP-rewrite, since improving content selection was the other motivation for exploring rewrite.

we are interested in the change in content selection between SUM Sum and SUM Id (the extractive version of the summarizer).


While these results are rather encouraging, they were not optimal.

we were surprised that semantic information performed worst than a simple word-based approach.


while still ensures the relatedness between a tuple and its contexts.

a tuple is contextually related to other tuples by two quantifiable contextual relations: argument-similarity and position-similarity.


In this paper, we focus on the probabilistic model instead of selecting the best translation.

we need to calculate the probability of the occurrence of word w e in language e given a document d in language f , i.e.


We can assume that each word w e in language e is independently generated by a finite mixture model as follows: Therefore we can use EM algorithm to estimate the parameters of the model.

speaking, we can iterate the following two step for the purpose above.


Standard evaluation benchmark is not available and thus we developed a test data from the Internet, containing Chinese Web pages and English Web pages.

we applied RSS reader 1 to acquire the links to the needed content and then downloaded the Web pages.


As a result, 100 million web standard format data were obtained.

the remaining 118 million web pages were regarded as non-Japanese pages by our tools.


In this paper, we propose an automatic estimation method for word significance (weights) based on its influence on IR.

weights are estimated so that evaluation measures of ASR and IR are equivalent.


A word weight should be defined based on its influence on IR.

weights are estimated so that WWER will be equivalent to an IR performance degradation.


Our method is an intermediate approach that lies between these.

it is more robust than rule-or grammar-based approaches and takes less effort than stochastic techniques.


However, recognition of such prosodic features poses significant challenges due to differences in surface realization from the underlying form.

context plays a significant role in prosodic realization.


We exploit both lexical and prosodic features for prosodic labeling of broadcast news speech.

in contrast to (Gregory and Altun, 2004), we employ a rich acoustic feature set, designed to capture and compensate for coarticulatory influences on accent realization, in addition to word-based features.


We compare their individual effectiveness as well as the effect of combined feature sets on labeling.

we consider both the binary accented/unaccented assignment task for pitch accent and the four way -high/downstepped high/low/unaccented -contrast to compare effectiveness in problems of different difficulty.


The results support our hypothesis that texts of electronic negotiation have different characteristics than records of face-to-face negotiation.

messages exchanged later in the process are more informative with regard to the negotiation outcome than messages exchanged at the beginning.


This paper addresses the problem of text mining in the digital devices domain.

we address the task of detecting semantic relations between digital devices in the text of Web pages.


We now describe the background in probabilistic models as well as different approaches to parameter estimation for probabilistic models.

we describe Naïve Bayes, logistic regression (analogous to conditionally trained Naïve Bayes) and then introduce Naïve Bayes trained to maximize the margin.


We consider the problem of 1 identifying product features and opinion words in a unified process from Chinese customer reviews when only a much small seed set of opinion words is available.

we consider a problem setting motivated by the task of identifying product features with opinion words and learning opinion words through features alternately and iteratively.


With the co-occurrence characteristic, identifying product features and opinion words could be combined into a unified process.

it is helpful to identify product features by using identified opinion words and vice versa.


We can use the majority voting by those (possibly polarity-shifted) sentiment words.

we first classify each sentiment word in the sentence according to whether the polarity is shifted or not.


Our approach is similar to that of Liu and Gildea (2007) except that in our case all the metrics in the combination are equally important 3 .

uLC is indeed a particular case of a parametric scheme, in which the contribution of each metric is not adjusted.


We have also studied the portability of optimal metric sets across combination strategies.

although QUEEN and ULC are thought to operate on metric combinations respectively optimized on the basis of human likeness and human acceptability, we have studied the effects of applying either measure over metric combinations optimized on the basis of the alternative metaevaluation criterion.


even though a single phrase in a source language sentence maps onto multiple phrases in a foreign language sentence, the phrases might not be paraphrases.


Under the constraint of the paraphrased dependency relations, we again try to acquire paraphrases at a phrase level.

we extract the phrases sharing the same head/modifier phrase in paraphrased dependency relations as a phrase paraphrase under a given bilingual dependency context (see eq.


For our development we have used a Java based opennlp MaxEnt toolkit 1 to get the probability values of a word belonging to each class.

given a sequence of words, the probability of each class is obtained for each word.


Secondly, the learning loop can end if no uncertain unlabeled examples can be found in the pool.

all informative examples have been selected for annotation.


It seems feasible that a separate validation set can solve both problems.

the active learning process can end if there is no significant performance improvement on the validation set.


In active learning process, our interest is to estimate the classifier's expected error on future unlabeled examples in the pool U.

we can stop the active learning process when the active learner results in the lowest expected error over the unlabeled examples in U.


Comparing with the baseline stopping criterion, a better stopping criterion not only achieves almost the same performance, but also has needed to learn fewer unlabeled examples when the active learning process is ended.

for a stopping criterion of active learning, the fewer unlabeled examples that have been leaned when it is met, the bigger reduction in human labeling cost is made.


In Co-EM, Learners A and B are trained on labeled set predicted by each other's view, with their models being updated in sequence.

the Co-EM algorithm interchanges the probabilistic labels generated in the view of each other before a new EM iteration.


The 'pure' corpus consists of abstracts that have the exact four section labels.

this corpus does not include AIM or PURPOSE sentences even though they are equivalent to OBJECTIVE sentences.


A recent survey (Lai and Bird, 2004) found that currently available XML-based corpus query tools support queries operating on multiple linguistic levels only in very restricted ways.

problematic are intersecting hierarchies, i.e., tree-shaped analyses on multiple linguistic levels.


As in ordinary ontologies, we will assume that properties of superordinate classifiers can be inherited to their subordinate classifiers.

a classifier taxonomy suggests transitivity of agreement with nouns over superordinate-subordinate relations as In order to evaluate the structures of our taxonomy, we verify the validity of transitivity.


On the other hand, the "indirect" case includes the relations which can not be extracted from the database but only inferred by using transitivity on the taxonomy.


In Figure 1 we observe that even as the other methods retrieve more documents, ∼ 90% of the questions still benefit from the cluster-based method.

the cluster-based method generates queries using a different type of content and in turn, these queries retrieve a different set documents than the other methods.


Given an entity pair, documents describing the entity pair are extracted from the Web via commercial search engines using both entities as the query.

snippets returned from the Web are treated as documents related to the query.


For an entity pair, E = {e 1 , e 2 }, where e 1 and e 2 are named entities, it is difficult to find sufficient sentences to describe their relationship from existing annotated corpora.

given an entity pair without a relation label, users cannot recognize the pair.


To capture the interaction between two entities, we send both entities, e 1 AN D e 2 , to commercial search engines and collect returned snippets as training documents for an entity pair, E. Snippets of returned search results are defined as the surrounding contexts of queries highlighted by commercial search engines.

the full texts of search results are not considered in the collected corpus when filtering noisy information in full documents.


Thus, we let the expected number of words generated from latent topics be features of each entity pair.

an entity pair E is represented as a feature vector whose length is equal to the number of latent topics and whose i th attribute is equal to where α i is the i th prior Dirichlet parameter.


To determine the parameters in ν-SVM, i.e., γ and ν, we observed the performance of the ν-SVM classifier by randomly selecting 80% of the sentences in the RDC corpus as training data and the remaining 20% as test data.

we applied five-fold cross validation to build a temporary model for parameter estimation.


This may be due to the latent topic f eature s ability to capture information at the semantic level precisely, but it cannot distinguish the information at the word level easily.

the latent topic f eature can capture the common semantic information, proba-bly the Role, of all sub-relations, but it cannot tell the difference between citizen − of and f ounder.


PPM is designed to predict the next c r by estimating the n maxgram probability P (c r |c r−1 r−n max +1 ) using backing-off techniques with regard to the current context.

the probability is estimated as a weighted sum of different (n + 1)-gram probabilities up to a fixed n max -gram as follows: The weights w n are determined through escape probabilities.


We make use of the automatically induced hyponym/hypernym relations in each language to create a transitive "bridge" for dictionary induction.

the dictionary induction task relies on the key observation that words in two languages (e.g.


To be able to automatically create taxonomies such as WordNet, it is useful to be able to learn not only hyponymy/hyponymy directly, but also the additional semantic relationships of meronymy and taxonomic cousinhood.

given a pair of words (X, Y), the task is to answer the following questions: 1.


The focus of the first approach is on a rule-based method with novel rule sets which we fine-tuned for legal domains.

we frame text segmentation as a rule learning problem.


The prior probability P (D) is usually assumed to be uniform and a language model P (Q|D) is estimated for every document.

they estimate a probability distribution over words for each document and calculate the probability that the query is a sample from that distribution.


(Harris, 1957) (4) a. X wrote Y ⇔ X is the author of Y b. X solves Y ⇔ X deals with Y (Lin and Pantel, 2001) The weakness of these templates is that they should be applied only in some contexts.

the lack of applicability conditions for slot fillers may lead incorrect paraphrases.


In future, we plan to address several issues such as those described in Section 6.

at present, the coverage and portability are of our interests.


It expands the set of training NCs from a relatively small number of manually-tagged seed instances.

it makes use of extra training instances fashioned through a bootstrap process.


For example, assuming automobile factory with the SR MAKE were a seed instance, NCs generated from synonyms, hypernyms and sister words of its constituents would be added as extra training instances, with the same SR of MAKE.

we would add car factory (SYNONYM), vehicle factory (HYPERNYM) and truck factory (SISTER WORD), for example.


Hence, we progressively reduce the similarity threshold, and incorporate higher-similarity instances into our training data earlier in the bootstrap process.

we run the CONSTITUENT SIMILARITY method and acquire NCs with similarity equal to or greater than a fixed threshold.


In linguistics, stem is a form that unifies the elements in a set of morphologically similar words (Frakes and Yates, 1992), therefore stemming is the operation which determines the stem of a given word.

the goal of a stemming algorithm is to reduce variant word forms to a common morphol o g i c a l r o o t , c a l l e d " s t e m" .


Our algorithm is bottom up while affix stripping methods are top down.

we try to generate the word using candidate stems of the word which we call cores of the word.


A cluster represents a set of morphemes that affects role of them in the word.

each morpheme could be applied as one or more roles for generating words.


For example, in a segmentation task using the IOB2 notation, the k-th local feature in f (x, y, i) can be defined as if x i is the word "boy", and y i is the label "B" 0 otherwise With parameter w, the best output sequenceŷ for an input sequence x can be found by calculating the best score:ŷ 3 Online Training We propose to estimate the parameter w in an online manner.

we use the online passiveaggressive algorithm (Crammer et al., 2006).


In the classification task, the identified segments are classified into one of the possible named entity types or rejected.

in the segmentation task, the sentence x are segmented bŷ where F s (•) is the set of segment features, and w s is the parameter for segmentation.


All of the experiments were run on a Unix machine with a 2.8 GHz CPU and 16 GB RAM.

the CRF trained by maximumlikelihood uses the L-BFGS algorithm (Liu and No-cedal, 1989), which converges quickly and gives a good performance on maximum entropy models (Malouf, 2002;Sha and Pereira, 2003).


For simplicity, our system uses correct NE tags when training.

as a feature for "Kawasaki" in the second sentence of (2), the correct feature "B-PERSON" is always added when training, not always added when analyzing.


Since SVMs do not output probabilities, our system uses the SVM+sigmoid method (Platt et al., 2000).

a sigmoid function s(x) = 1/(1+exp(−βx)) is applied to map the output of SVM to a probability-like value.


Thirdly, the utterance itself can be purely free-style and far beyond the formal grammar.

self-defined or symbolic languages may be used in the dialogues.


If the intention annotated by human is Computer and Internet, then the system starts proposing a correct intention from the 5 th utterance.

the information in the first 4 utterances is not sufficient to make any decision or make wrong decision.


The hit speed evaluation tells us the system can start making good decision when near only 37.46% of total utterances are processed.

the recommended advertisements can be placed to attract users' attentions in the rest 62.54% of total utterances.


The sub-alignments are restricted to those that can be described by the two basic operations.

for our ITG-like tree, the nodes are mostly word pairs, except some indecomposable word sequences pairs.


Having defined such labels, the prediction of tree structures is transformed to a label prediction one.

giving word pairs as input, we transform them to their corresponding labels (stack operations) in the output.


We also show that complex LMs can be used in re-ranking to improve performance given a strong baseline.

we use high-order n-grams continuous space LMs to obtain MT of the well-known NIST 2006 test set that compares very favorably with the results reported in the official evaluation.


First, the hyperlink activation is not assisted.

imprecise, poor or no information is provided to the reader before s/he activates the link.


A set of concepts and models of textual interpretation has been worked out, including for example, anaphora, connectors, mental spaces, etc.

these studies have shown that even for languages apparently close like French and Danish, texts are not organized in the same way (Lundquist, 2005).


Although many studies for Japanese "basic vocabulary" have been done (National Institute for Japanese Language, 2000), "basic vocabulary" in the studies means a vocabulary which children or second-language learners have (or should learn).

the aim of such studies is to determine a set of headwords which should be included in a Japanese dictionary for children or second-language learners.


Evaluation conferences and contests on question-answering systems have also been held.

the U.S.A. has held the Text REtrieval Conferences (TREC) (TREC-10 committee, 2001), and Japan has hosted the Question-Answering Challenges (QAC) (National Institute of Informatics, 2002) at NTCIR (NII Test Collection for IR Systems ) 3.


x i (1≤i≤K) represents the K summary sentences, x i (K+1≤i≤K+N+1) represents the N sentences to be classified.

the first K sentences are labeled sentences while the remaining N sentences are to be re-ranked.


These desiderata have lead to an increased interest in developing unsupervised WSD methods, flexible relative to the word sense inventory, and which disambiguate all open-class words in a given context as opposed to a selected few.

appropriate from this point of view are graph-based methods (Navigli and Lapata, 2007), which map the open-class words in a given context onto a highly interconnected graph.


Because the sense inventory for the Senseval data comes from WordNet and we work at the sense level, we use relatedness measures based on WordNet, as opposed to corpus-based ones.

in the experiments presented further in the paper, we have used a relatedness measure based on hypernym and hyponym information, in the following manner: if the sense s i w 2 of w 2 is a hypernym of the sense s j wx or a close hyponym (distance at most 2) or connected through a similar to/antonym of relation, we consider the two senses related and relatedness gets a score of 1.


Next, we replicate the Banerjee and Pedersen (2002) method in extending the glosses to include words from the definitions for the (immediate) hypernyms and/or hyponyms of each sense s i,j .

an extension of the Banerjee and Pedersen (2002) method which makes use of the sense-annotated definitions is to include the words in the definition of each sense-annotated word rather than traversing the ontology relative to each word sense candidate s i,j for the target word w i , we represent each word sense via the original definition plus all definitions of word senses contained in it (weighting each to give the words in the original definition greater import than those from definitions of those word senses).


(6) is only 4 times (2*2) slower than the GTK in this case.

we can say that given the constraint that there is only one optional node in one rule, the time complexity of the GTK is also O(|N 1 | • |N 2 |) 5 , where N 1 and N 2 are the numbers of tree nodes, the same as the TK.


By this approach, we may achieve better results with using vocabulary of standard size (< 100k), since words in "correct" parts of errgrams may include words that are not in the limited size vocabulary used for the first recognition pass.

we can partially reduce the OOV problem by this approach.


is the hypothesis function (a classifier like AdaBoost, SVM etc).

if P r[y = h(f (x))] is found to be sufficiently high, it means that the feature f (x) is able to distinguish effectively between the actual language L and the approximate languagẽ L. f (x) captures those features of the language that are complementary to the ones captured by the trigram model and therefore f (x) is a good feature to augment the trigram language model with.


This implies that the ratio of trigram to quad-gram perplexities would be lower for a fake article than for a real article.

this ratio is similar to computing the likelihood ratio of an article w.r.t the trigram and quad-gram models.


Owing to the use of only the short term word history for computing the probability distribution of a language, a trigram model fails to model semantic coherence and we exploit this fact for the classification task.

we intend to model both intra-sentence and inter-sentence semantic coherence and use them as features for classification.


A binary classifier using f (x; θ) outputs a predicted class label assignmentŷ for x asŷ (n)  An LRM is a binary classifier that uses the discriminative function f (x; θ).

in this model, the class posterior probability distribution is defined by using a logistic function: P (y = 1|x; θ) = g(f (x; θ)) and P (y = 0|x; θ) = 1 − P (y = 1|x; θ) = g(−f (x; θ)).


A set of rules that are intended to be interpreted in a sequence is called a decision list.

a decision list is an ordering of the given set of rules.


Corollary 1 If NP = P then for any > 0, the MAXDL problem cannot approximated within a factor of n 1− .

the problem is not approximable within any constant factor.


We show that whereas some kernels are less effective than others, they exhibit properties that are complementary to each other.

we found that relation extraction can benefit from increasing the feature space through convolution kernel and introducing bias towards more syntactically meaningful feature space.


The performance of subsequence kernel is not as good as the dependency path kernel, but the difference is small.

the subsequence kernel gave the best recall, whereas the dependency path kernel gave the highest precision.


We conducted experiments on the standard ACE 2003 evaluation set and showed that whereas some kernels are less effective than others, they exhibit properties that are complementary to each other.


Then two words w i , w j are defined to be co-occurring in a url is true for at least one pair of anchor texts (a i , a j ).

two words are said to co-occur in an url if at least one inbound pair of anchor texts contains the two words.


Thus, this study regards acronyms (with and without translation) and alias as Japanese Table 2: Top 10 frequent parenthetical expressions used in Japanese newspapers from 1998-1999 abbreviations in a broad sense, based on their interchangeabilities.

the goal of this study is to classify parenthetical expressions X (Y) into true abbreviations (groups I, II, III) and other usages of parentheses (group IV).


A tree graph is essentially a kind of dendrogram, which means clustering results can be generated solely by making a cross cut at some height between the root and the leaves.

as there is no horizontal connection at the same level, it is not possible to create triangle circulation paths in a single stroke.


This principle can be called Branching Markov Clustering (BMCL) in the sense that it makes it possible to correct for unbalances in cluster-sizes by dividing large Markov clusters into appropriate branches.

bMCL is a way of rebuilding adjacency relationships "inside" MCL clusters, by making reference to "outside" path information.


Lexicalized HMM tagging not only considers the POS tags information to determine whether b is noun, adverb or nothing, but also considers the lexical information a itself.

it considers the probabilities P r (noun|a, verb), P r(adverb|a, verb) and P r(nothing|a, verb) for instance.


Lexicalized HMM is an improvement to the standard HMM.

it substitutes the probability P r the tag of word w i is determined by the tags of the J words right before w i and L words right before w i .


The 'or' node can allow us to use higher ngram model although the training corpus may be not sufficient.

it can overcome the data sparseness problem and make use of more information from the training corpus.


The SRL using TSVM is related to following portions: Dataset The principle of TSVM described in above section implicitly indicates the performance depends deeply on dataset (including tagged and untagged data).

tagged data have an influence on original regular SVM in the first step of training, while the untagged data will affect the final performance through the iteration of training.


In this article we address the task of automatic text structuring into linear and nonoverlapping thematic episodes at a coarse level of granularity.

we deal with topic segmentation on multi-party meeting recording transcripts, which pose specific challenges for topic segmentation models.


it uses a convex combination of a set of component distributions to model the observed data.

each word in a block of text is seen as a sample from a mixture model, where mixture components are multinomials P (w m,n |z k ) and the mixing proportions are P (z k |b m ).


Then, we use these posterior probabilities as a threshold to identify the boundaries of thematic episodes via support vector classification (Georgescul et al., 2006).

we consider the topic segmentation task as a binary-classification problem, where each utterance should be classified as marking the presence or the absence of a topic shift in the dialogue.


For each word w m,n from a block of text b m , a topic indicator k is sampled for z m,n according to the block-specific mixture proportion θ m .


As in Section 2.1.2, the last step of our system consists in employing binary support vector classification to identify the boundaries of thematic episodes in the text.

we consider as input features for support vector learning the component values of the vector (θ m,z 1 , θ m,z 2 , ..., θ m,z k ).


The evaluation phase consists in checking the performance of each model for predicting thematic boundaries.

we check the performance of the models for predicting thematic boundaries on the same test set.


Note that the P k error allows for a slight variation in where the hypothesized thematic boundaries are placed.

wrong hypothesized thematic boundaries occurring in the proximity of a reference boundary (i.e.


In contrast, AMDD has better performances for less dimensionality reduction.

the LDA performance curve goes down when the number of latent topics exceeds over 300.


The screen reader reads aloud text within a document, and it also reads aloud information within dialog boxes and error messages.

the primary function of any-screen reading system is to become the "eye" of the visually impaired computer user.


This is done by taking advantage of the shortest dependency path between two involved proteins in the dependency parse tree structure of a sentence.

only the words appearing on the shortest dependency path and their associated constituents in the constituent parse tree are considered as necessary and thus kept as the essential part of the constituent parse tree.


(2005a) adopt a generalized substring kernel over a mixture of words and word classes to extract protein interactions from biomedical corpora and semantic relations from newswire corpora.

they achieve the F1-score of 54.2 in extracting protein interactions from the AIMed corpus.


in the AIMed corpus In this paper, we make use of the shortest dependency path in the dependency tree to refine the constituent parse tree.

all the words which appear on the shortest dependency path, together with their associated constituents, are kept in the constituent parse tree, while other constituents are removed, forming a Shortest Dependency Path-directed Constituent Parse Tree (SDP-CPT).


(2008) propose a dynamic syntactic parse tree (DSPT) by exploiting constituent dependencies to refine the constituent parse tree.

they manually devise five categories of constituent dependencies, motivated by various kinds of lexical dependencies.


It is generally acknowledged that the Stanford scheme is closer to the targeted semantic representation from the perspective of relation extraction (Buyko and Hahn, 2010).

among the four styles of Stanford representations, "collapsed dependency" can much simplify patterns in relation extraction since dependencies involving preposition, conjunct as well as referent of relative clause are effectively collapsed to reflect direct dependencies between content words.


Considering the importance of dependency path in PPI extraction and the effectiveness of employing dependency information to refine the constituent parse tree for tree kernel-based semantic relation extraction in the newswire domain, it is a natural idea to automatically generate the proper constituent parse tree with the help of the shortest dependency path.

we can reshape the constituent parse tree by making use of the shortest dependency path between two proteins.


3) For each word along the SDP, add the corresponding leaf word node and its upper constituents to the SCP.

a) when the dependency type is "prep_xx", such as "prep_of", the preposition xx and its associated constituent are also added; b) when the word to be added is outside the SCP, a new path from the current lowest common ancestor to one of the added words' ancestors is also added.


In order to fairly compare our work with other PPI extraction systems, we use five PPI corpora, i.e., AIMed (Bunescu et al., 2005a), BioInfer (Pyysalo et al., 2007), HPRD50 (Fundel et al., 2007), IEPA (Ding et al., 2002) and LLL (Nédellec, 2005).

most of the evaluation is done on the widely-used AIMed corpus, which contains 177 Medline abstracts with PPI instances, and 48 abstracts without any PPI instances.


Totally, there are 4,084 protein references and around 1,000 annotated protein-protein interactions in this data set.

in this paper, a potential PPi instance is generated for any pair of two proteins in a sentence if a sentence contains n proteins, ( n 2 ) protein pairs are generated.


In this paper, a potential PPI instance is generated for any pair of two proteins in a sentence That is, if a sentence contains n proteins, ( n 2 ) protein pairs are generated.

all the self-interactions (59 instances) are removed and all the PPI instances with nested protein names are retained (154 instances), as adopted in most literature.


In our experimentation, we select Support Vector Machines (SVM) as the classifier since SVM represents the state-of-the-art in the machine learning research community.

we use the SVM light (Joachims, 1998) with the convolution tree kernel function SVM light -TK (Moschitti, 2004) 2 to compute the similarity between two constituent parse trees.


Evaluation is done using 10-fold documentlevel cross-validation, each of which contains 90% of documents as the training data and 10% as the test data.

for the AIMed corpus we apply the exactly same 10-fold split as widely used in a series of relevant studies (e.g., Bunescu et al., 2005a;Giuliano et al., 2006).


The percentage of occurring frequency with which each minor type is employed when generating the SDP-CPT with respect to the total number of dependency tuples is listed in Column 2.

the tree setup without using any dependency type, which corresponds to that with the length of 0 (SCP) in Table 1, is displayed at the top row.


Table 2 shows that with the addition of all Argument types and all Modifier types, the SDP-CPT attains the best performance of 59.1/57.6/58.1/83.3 in P/R/F1/AUC as shown in bold fonts, outperforming the SDP-CPT with all dependency types added (59.6/54.3/56.7/82.7 in P/R/F1/AUC).

it shows The dependency types of subj, obj, prep and nn yield substantial performance improve-ment both in the accumulative mode and in the individual mode; The dependency types of Conjunction and Others harm the performance in the accumulative mode, though Conjunction improves the performance in the individual mode; It is interesting to note that while the dependency types of arg-others and mod-others harm the performance in the individual mode, they slightly improve the performance in the accumulative mode.


Table 3 compares the performance of F1-score (outside parenthesis) and AUC (inside parentheses) between SDP-CPT and the previously-used tree setups across major PPI corpora.

sPT is used as a baseline and for comparison we re-implement two other effective tree setups for semantic relation extraction in the newswire domain, i.e.


It shows that our individual kernelbased system performs better than all the other individual kernel-based systems on the AIMed corpus.

our SDP-CPT kernel significantly outperforms the Partial Tree kernel over constituent parse trees (Tikk et al., 2010).


Legal Engineering regards that laws are a kind of software for our society.

laws such as pension law are specifications for information systems such as pension systems.


To implement the localize() method, we use the approach described in (Wu et al., 2010a).

we replace the pointer values of HEAD and SEM HEAD features in non-terminal nodes with three labels: "S" for single daughter, "L" for the left-hand-side daughter, and "R" for the right-hand-side daughter.


From this table, we find that the ratio of the number of arguments in the three sets are approximately similar.

nearly half of the   predicate words have one argument.


(2007), and preliminary experiments on a single language pair show improved performance.

the model in (Li et al., 2007) is explicitly aimed at long-distance reorderings (English-Chinese), prunes the alignment matrix gradually to fit the source syntactic parse and employs Maximum-Entropy modeling to choose the optimal local ITG-like permutation step of sister subtrees but interleaves that step with a translation step.


one to use based on the referring expression.

the pronoun model is selected if a referring expression is a pronoun, and the nonpronoun model otherwise.


It also shows that both the combined and separated models obtained the best performance for each model using all the features.

the three types of features work in a complementary manner on multi-modal reference resolution.


Finally, for the comparison with KyTea, we use a publicly available corpus, the Balanced Corpus of Contemporary Written Japanese (Maekawa (2008)).

we use the 2009 Core Data of this corpus, which consists of 37k sentences annotated with pronunciations (referred to as "BCCWJ").


If an instance SC (including the segment k sc ) is much related to the input NE C (including the segment k c ), this case suggests that the semantic similarity between k c and k sc is much high.

the two probably have the same translation k se .


Recent growth of the web has opened the possibility of using the internet to break the barriers of space and time.

social network service (SNS) has begun to receive a lot of attention recently.


Therefore, the total number of corrected sentences amounts to 1,288,934.

one sentence gets corrected approximately 1.69 times on average.


Thus, as a result of this step each instance x is now represented by m tuples of vectors, the first level feature vectors x i 1 , ..., x i |x i | and the newly generated meta feature vectors In our problem of AA, we consider four types of first level features: stylistic features, lexical features, syntactic features, and perplexity values from character 3-gram language models.


When the threshold is above 32, there are rarely word pairs satisfying the threshold.

there would be no coherence constraints.


In Entity Extractor, an NER tool developed at UIUC (Ratinov, 2009) 3 is employed.

phrases/words tagged with PER, ORG, LOC and MISC tags are extracted when the target entities are people, organizations, locations, and products, respectively.


To satisfy this need, we develop a very simple parse model that penalizes any non-normal-form rule applications, effectively relying on the CCG supertags to identify likely grammatical relations.

combinators like function composition and type raising are penalized by a fixed amount, while function application is allowed to pass without penalty.


For completeness, we briefly explore another option, even simpler than this: we trained SRL models that relied on no syntactic features at all.

we included the word, predicate, and before/after features (described in detail in section 3).


We propose an approach to Japanese predicate argument structure analysis exploiting argument position and type.


BL: This model has a single step in 'classification' step.

'selection' step in this model selects the most likely argument from all noun phrases preceding the predicate.


We improved Japanese predicate argument structure analysis exploiting argument position and type.

we proposed two methods: the improved similarity measure between argument positions and the selection-and-classification approach considering argument type.


In fact, the accuracy of NE alignment is only affected by the boundary alignment of English and Chinese NEs.

we do not care about how to align within or without the NEs.


Since our alignment is bidirectional, we merge the probabilities in two directions to come up with formula 7.

we set zero while the translation pair "s → t" does not exist in the translation table given by GIZA++.


Therefore, combining the translation-based language model with the latent topic model with categories, we can get the ranked list of semantically similar questions (Q 4 , Q 5 , Q 2 , Q 3 ) which are better than the previous retrieval result.


Although we extract test instances in a similar manner, we do not count characters originally with the mark at testing.

we evaluate the accuracy only on unmarked characters present in real world setting.


Following the previous work (Zhao et al., 2006;Zhao et al., 2010), we employ the linear chain CRFs (Lafferty et al., 2001) as our learning model.

we use its CRF++ (version 0.54) implementation by Taku Kudo.


By using the baseline segmenter, each character c i in the unlabeled data is labeled with a tag t i .

the output of auto-segmentation is a sequence {(c i , t i )} L i=1 .


If there is no matching word, we proceed to the next character.


Since nodes on the same local hierarchy share the same local scope, local feature selection process for all nodes of SOT is achieved in local hierarchy based manner.

for the feature selection process on a local hierarchy ∆, let c 1 , c 2 , ..., c K denote the K nodes on ∆.


5 it is observed that DF, MI, and TS feature selection algorithms achieve generally better performances than CHI and IG feature selection algorithms when they are employed in the proposed LFS framework.

the TS algorithm is generally the best among the five employed algorithms while the DF algorithm can also achieve as comparable good performance as the TS algorithm does.


is also the defined opinion scoring function then ops(r) is defined as in Formula (4).

the opinion score of a dependency relation is an average of the aggregate scores of its descendent dependency relations.


By following the compositionality-based polarity decision rules, the polarity of a noun or a noun phrase that has no inherent polarity is determined by its modifier's polarity.

the polarity of the modifier is propagated to the upper level node in the dependency tree structure.


For the relation descriptor extraction problem, however, we expect that there is either a single relation descriptor sequence or no such sequence.

for a given relation instance, we only expect two kinds of label sequences: (1) All y i are O, and (2) exactly one y i is B-REL followed by zero or more consecutive I-REL while all other y i are O.


So at the training stage, we should also consider onlyỸ by defining the normalization termZ as follows: The difference between Equation 5and Equation (2) is the set of label sequences considered.

while in linear-chain CRFs the correct label sequence competes with all possible label sequences for probability mass, for our task the correct label sequence should compete with only other valid label sequences.


The focus on expanding the input textual data gives a generally-applicable approach, which can be applied to existing methods for attribute acquisition from query logs, to increase coverage.

the application of previously-proposed extraction patterns (Paşca and Van Durme, 2007) to the expanded set of queries allows for the acquisition of additional attributes that would otherwise not be acquired only from the set of known queries.


However, the computation can be translated into parallelizable operations in a distributed computing framework such as Hadoop (White, 2010) or MapReduce (Dean and Ghemawat, 2004).

the aggregation of queries into templates can be performed in a single MapReduce step.


Our model assigns a probability to each possible dependency structure, T , and case structure, L, of the input sentence, S, and outputs the dependency and case structure that have the highest probability.

the model selects the dependency structure T best and the case structure L best that maximize the probability P (T, L|S) or its equivalent, P (T, L, S), as follows: The last equation follows from the fact that P (S) is constant.


This SVMbased parser was trained on a newspaper corpus, while the test sets were obtained from patent filings and the web because tagged corpora of these domains that are large enough to train a supervised parser are not available.

our proposed model achieved a good performance on the patent set without using in-domain corpora.


However, other than its superior empirical performance to competing coreference models (such as the MP model), little is known about the joint CR model.

most of the linguistic features for coreference resolution were developed and evaluated in the context of the MP model, and thus it is not clear whether these features would fare similarly when used in combination with the joint CR model.


Our experimental results indicate that while both the tree-based and path-based features improve coreference performance when applied to a Baseline feature set in isolation, the best performance is achieved when they are applied in combination.

these two types of features yield an improvement of 2.2-3.7% in F-measure over the Baseline joint CR model, which corresponds to a 8.6-11.7% reduction in relative error.


(2007), we create cluster-level features from the relational features in our 39-feature set using four logical predicates: NONE, MOST-FALSE, MOST-TRUE, and ALL.

for each relational feature X, we first convert X into an equivalent set of binary-valued features if it is multi-valued.


We follow Rahman and Ng's (2009) method for creating training instances.

for each NP, NP k , we create a training instance between NP k and each preceding cluster c j using the features described above.


Not only can parse trees be exploited to identify coreference relations via the extraction of paths, but they can be used to determine the anaphoricity of an NP.

we aim to identify nonanaphoric NPs by employing parse trees as structured features.


Of course, having an SVM learner learn a ranking model from both the flat and tree-based features requires more than just adding the tree-based features to the feature set.

we need to implement the three steps below.


However, if one of the feature vectors has a tree-based feature 3 (which happens when c i or c j is NULL), we handle the flat features and the tree-based feature separately.

we first perform subtraction for the flat features as described above, and then append the tree-based feature to the feature set of inst.


In this case, we need to (1) compute the similarity of their flat features and the similarity of their tree-based features separately, and then (2) employ a composite kernel, K c , to combine the two similarity values.

we define K c as follows: where F 1 and F 2 are the full set of features (containing both flat and structured features) that represent the two instances under consideration.


K 2 is a convolution tree kernel (Collins and Duffy, 2001), which operates on the tree-based features.

k 2 computes the similarity of two parse trees by efficiently enumerating the number of common substructures in them.


Results on 147 Switchboard dialogues, show that both types of features are effective at improving the performance of the clusterranking model.

when they are applied in combination, we see a reduction in relative error by 8.6-11.7%.


Our second baseline is the joint CR model, which is trained using the method described in Section 2.2.

this baseline model does not employ any tree-based or path-based features.


In comparison to the results of the Baseline joint CR model in row 2, we can see that adding the path-based features into the feature set improves the joint CR model according to both scorers.

b 3 and CEAF F-measure scores rise by 1.3% and 2.1%, respectively, suggesting the usefulness of the path-based features.


In comparison to the results of the Baseline joint CR model in row 2, we can see that adding the trees and the unigrams into the feature set improves the joint CR model according to both scorers.

b 3 and CEAF F-measure scores rise by 1.0% and 1.9%, respectively.


To evaluate the performance of the noise detection enhanced spectral clustering approach, we compare the ROUGE scores of it with the ROUGE scores of the LexRank approach for generic summarization and query-oriented LexRank approach, which is a direct extension of LexRank in our clustering and ranking frameworks.

the sentence clusters are generated by the traditional spectral clustering algorithm first and then the sentences within each cluster are ranked with LexRank.


The experimental results on the DUC summarization datasets demonstrate the effectiveness and the robustness of the proposed approach.

the contribution of noise detection and the query information in the clustering process are clearly observed.


Our proposed method evaluates the importance of each utterance from the standpoint of call agents who report calls for managing or analyzing calls.

the proposed method includes information frequently reported by call agents in summaries using past call logs commonly recorded in the contact center.


To deal with the above paraphrases, we expand expression (3).

the likelihood that the meaning of a word in a dialogue is reported in past logs as a set of words is calculated as the sum of expression (3) for each word in as follows: In conclusion, , the report score of a word , is estimated by expression (4), where is the set of all words in past logs.


Next, we examined the performance of indicative summarization for each competing method.

we summarized the dialogues in the test data by each method at a 30% compression rate, and then evaluated their results.


Table 4 shows that there are few frequent utterances in the summary generated by the AS method when the compression rate is low.

the result indicates that the AS method (without RFU) can eliminate uninformative frequent utterances when generating an indicative summary or informative summary with a low compression rate.


This paper studies a less ambitious problem: we align an alreadyexisting hierarchical browsing structure, e.g., the electronic slides of lecture recordings, with the sequential transcripts of the corresponding spoken documents, with the aim to help index and access such archives.

we study a number of semantic tree-to-string alignment models with different modeling capabilities and time complexities in order to obtain a comprehensive understanding of these models and hence the indexing task itself.


In this paper, we discuss two more quadratic-time models to bridge the gap.

this paper studies nine different models, with the aim to provide a comprehensive understanding of the questions discussed above.


However, when the task involves the understanding of high-level discourse, it becomes more challenging than finding local discourse conveyed on small spans of text; e.g., the latter is more likely to benefit from the presence of discourse markers.

for spoken documents, speech recognition errors, absence of formality and thematic boundaries, and less linguistically well-formedness of the spoken language, will further impair the conditions on which an reliable discourse-analysis algorithm is often built.


Note that there are more than one way to conducting such a search, but they should yield the same results once the objective function, e.g., the normalized-cut score here, is the same.

the Main function in Figure 1 takes as input an M × N similarity matrix, where, same as before, M and N denote the number of bullets and transcribed utterances in a lecture, respectively.


By the nature of the problem, words in a bullet could be repeated multiple times, even when the speaker traverses to talk about the descendant bullets in the depth of the sub-trees.

the content of a bullet could be mentioned not only before its children but also very likely when the speaker traverses to talk descendant bullets, if any, which violate the pre-order-walk assumption.


In our problem here, when a lecturer discusses a bullet, the words used are likely to be different from those used in another bullet, suggesting that the spoken documents themselves, when ignoring the alignment model above for the time being, could potentially indicate the semantic boundaries that we are interested in here.

the cohesion conveyed by the repetition of the words that appear in transcripts but not in slides could be additionally helpful; this is very likely to happen considering the significant imbalance of text lengths between bullets and transcripts, from which the alignment models by themselves may suffer.


where, In fact, a state-of-the-art topic-segmentation model (Malioutov and Barzilay, 2006) (also called a cohesion model in this paper) can be naturally incorporated into the graph-partitioning alignment models that we have discussed.

we can augment the SeqCut, HieCut, and P rsCut models with the cohesion models to form three new models SeqCutTpc, HieCutTpc, and PrsCutTpc, respectively.


The best alignment between paragraphs is determined by minimising a cost metric for alignments, based on the distribution given in Equation 3.1, and prior probabilities for different alignment types.

the cost of an alignment between a set of sentences E (of total length L 1 ) and a foreign set F (of total length L 2 ) is defined by is calculated by integrating the normal distribution given in Equation (3.1) over all values more extreme (further from the mean) than Alignment 0-1 1-1 2-1 2-2 Prior 0.010 0.890 0.089 0.011 Figure 1: Aligning words from non-standard orthographic transcriptions with the Gale-Church Algorithm, using normalised word lengths.


We use a greedy sampling strategy to ensure that some high likelihood models are included in the sample.


The document representation that we use is a mixture of byte n-grams (Cavnar and Trenkle, 1994; Baldwin and Lui, 2010a), as it is language-neutral in that it does not make any assumptions about the language or language type of each document.

we make no assumptions about word delimitation (e.g.


Until now, there is no other text search model that formally exploits and presents in documents and queries all abovementioned NE features or all above-mentioned WW features.

in a context, after a disambiguation process, if a WordNet word has more than one sense with the equally highest rank, then the most specific common hypernym (msc_hypernym) of those senses will be chosen and the word will be represented by the pair of that hypernym and the form of the word.


Specific sentences contain details and can be used to support or explain the general sentences further.

general sentences create expectations in the WSJ articles AP articles Agree total gen spec total gen spec  5  96  51  45  108  33  75  4  102  57  45  91  35  56  3  95  52  43  88  49  39  undecided  1  5  Total  294 160  133  292 117  170   Table 4: Annotator agreement and annotators were encouraged to use their intuition to choose a judgement.


(2006), the sentence location is an important feature, since it might be the most reliable information on citation function one could obtain from the paper directly.

we take the following physical features into account to distinguish between different citation functions: • Location: section category, belonging to one of the predefined six categories described in Section 2.3.


Besides, for all these datasets in different configurations, BayesNet and NaiveBayes are not very sensitive to the size of the training data.

these two supervised classification models can well learn our feature sets and make a good classification performance with only few training data (training ratio as 10% here, about 170 instances).


As in many other natural language processing tasks, large training datasets need to be annotated manually in order to improve the performance of citation classification.

a good annotation schema and abundant annotated training data are the basis for building a good automatic citation classification model.


Because annotation is time-consuming, we design a semi-supervised learning algorithm to make use of unlabeled data under small training datasets in citation classification.

we adopt the idea of selftraining (Zhu and Goldberg, 2009) to employ unlabeled data, but select new labeled data in the process of self-training in a more reliable way by taking advantage of the ensemble learning (Opitz and Maclin, 1999) algorithm.


So α is set to be 0.3 in case insufficient unlabeled data to be added as labeled data.

30% of the instances are randomly extracted as training set and the remaining instances are set as test data.


As mentioned before, self-training has a high demand on training data, especially initial labeled data.

the quality of the initial labeled data can largely affect the final overall performance.


With respect to the in-domain task, our results indicate that a text collection of homogeneous quality and style allows for high accuracy.

we correctly classify 81.9% of the music texts using a very efficiently computable feature set.


Based on the three language functions, Katharina Reiß (1971) defined a classification of text types, which relates to the intention of the author of a text.

she distinguished between the form-focused expression of the author's attitudes in expressive texts, the aim of making an appeal to the reader in appellative (or operative) texts, and the content-focused description of objects and facts in informative texts.


There, we see that feature type 1, 3, and 5 also performed best within the smartphone domain.

the writing style type (64.8%) is only 4% worse than the complete feature set.


In this paper, we investigate the parameter adaptation issue in SMT.

a transductive MERT algorithm is proposed to better explore both development and test datasets.


By this iterative search process, each particle can learn from the experience of other particles in the same population (called a swarm).

each particle in the iterative search process would adjust its flying velocity as well as position based on both its own acquaintance and the flying experience of other particles in the swarm.


9 We attempted to explicitly increase TER dissimilarity by discriminatively optimizing against the input system hypotheses.

10 we used the input hypotheses as reference translation and optimized in the opposite direction that we normally would.


"perform a ceremony" Figure 1: Simplified Entry from Iwanami Dictionary: toru "take" that combines automatic labeled data expansion and semi-supervised learning aiming a synergistic effect.

we propose a two-step approach: in the first step (Step-1), we automatically expand the labeled training data from raw corpora.


Therefore, we attempt to extract longer, more natural and high quality labeled data from the raw corpus, under strict conditions using fragmentary examples.

first, we extract example sentences (EX) from Iwanami.


Therefore, to investigate trends based on sense distribution, we employed the entropy E(w) of the frequency distribution in given training data, which is given by where p(s k |w) is the probability that word w will be sense s k .

p(s k |w) and then E(w) reflect sense frequency bias.


Difficult word means that many more senses appear in corpus.

more instances are needed to guess the sense correctly, that is the reason for our method is especially effective for such difficult words.


when using labeled data for semi-supervised learning, minimum expansion provides the best performance, and protects the system against sense bias.


According to the definition, it is desirable to use NGD to measure the relatedness between any term in the WSP and d. Note that we always compute the NGD scores in the context of ConceptNet assertion <c, relation, d>.

we only need to consider the relatedness between d and the term in WSP(c).


The corresponding Hindi synsets contain the same word, viz., saagar.

samudra in Marathi gets translated as saagar in Hindi irrespective of its sense.


For this reason, a bag-of-words model is also employed.

each context c i is associated with a set of words (g i ) selected in the corpus preprocessing stage.


We tried various parameter combinations shown in Table 1.

we selected the parameter combination p 1 =15, p 2 =10, p 3 = 0.05 that maximized the performance in this evaluation.


In this paper, we investigate the use of AL in event extraction.

we apply the active learning approach to the Attack event, because it is the most frequent event type in the ACE corpus, and is particularly challenging because of the large number of different expressions: there are 312 different words in the corpus that serve at least once as the main word (the "trigger") of an Attack event, After studying several sampling strategies, we settled upon a pseudo co-testing approach where a second classifier which solves a coarser variant of the original task is used.


This makes it difficult to split the feature set into two views.

the identity of the trigger will be a critical feature for any successful classifier.


Consider how much data must be annotated to get to 95% of full corpus score for trigger labeling (F-Measure 67.5%): mul-ti_pCT only takes 7 iterations; pCT takes 17 iterations; Uncertainty takes 38 iterations.

5.8%, 9.8%, 18.2% of the whole corpus needs to be annotated to reach the same performance.


In this paper, we study DS for designing endto-end systems of sentence-level RE.

we propose a joint model between Web data derived with DS and manually annotated data from ACE.


It indicates the inherent difficultly in parsing imperatives and questions; to match the tagging accuracy of the WSJ tagger for the WSJ (97.53% in Table 3), just using much more training data does not appear to be enough.

the problem is more serious for imperatives.


6 In both steps 2 and 3, the log-likelihood increases monotonically, and this algorithm thus always converges to an optimal solution but does not ensure the global maximum parameter will be assigned.

we can obtain convergence to different local optima.


The methods are compared based on the effect their application has on the parsing coverage and accuracy of the GG grammar of German (Crysmann, 2003).

we adapt the LA method of Cholakov and van Noord (2010) which was originally developed for the Dutch Alpino system to be used with the GG.


As can be seen, the number of phrase pairs is significantly increased after corpus expansion.

the sizes of the augmented phrase tables are increased by 56% and 171% for the 29k set, 25% and 132% for the full set, which prove that our sentence novelty model has made considerable contributions to the enrichment of phrase tables.


Our method also showed faster training and processing speeds than those of Linear-Chain and Semi-Markov.

our proposed method with RC showed about a 87 times faster training speed and about a 27 times faster processing speed than those of Linear-Chain.


SR showed faster training and processing speeds than those of SPJR and NECC using the SR-based base NE chunker.

sR showed about a 2.4 times faster training speed and about a 1.6 times faster processing speed than those of sPJR.


Formula L.2 Formula L.2 is a hard constraint that must always hold whereas the others are soft and can be violated.

the saliency property can be written as follows: Formula S.1: Saliency: if the KB entry is linked to an entity that precedes the current mention , and is a candidate KB entry of , then the current entity should also be linked to .


The final MLN-based configuration (MLN JOINT ) was constructed by adding the NILfiltering formulae to MLN DIS .

all formulae introduced in Section 3 were employed.


Because news comments are freely written without checking, they are very different from formal news texts.

named entities in news comments are usually composed of some wrongly written words, informal abbreviations or aliases, which brings great difficulties for machine detection and understanding.


Because news comments are freely written by different persons with different education backgrounds and writing styles, they are very different from formal news texts.

chinese news comments have the following properties: 1) The texts in news comments are very informal and noisy, especially for entity names.


All the three schemes for finding useful entities in the news article are helpful to the task.

the use of focused entities in the news article (Baseline3 + FocusedNE) can much outperform Baseline3 and the method using all named entities (Baseline3 + All NE) for person name recognition and organization name recognition.


In this experiment, languages were naturally arranged into language families.

all languages in the same language family, such as Sino-Tibetan and Tai-Kadai, are adjacent in the data.


On the contrary, the score propagation for hyper is done in a slightly different way.

the penalized distributional similarity is propagated to not only the ancestors but also the descendants.


The advantage of the proposed method compared to previous methods that use nothing but either distributional similarity of a trg or co-occurrence with their hypernyms via lexico-syntactic patterns (Snow et al., 2006;Yamada et al., 2009) (or both) was demonstrated as follows.

it was shown that many terms do not have reliable distributional similarity (owing to their infrequency in a corpus) and do not co-occur with their hypernyms via any lexico-syntactic pattern in a sentence.


An important challenge is thereby the alignment of differently represented word senses, which we address in this paper.

we propose a new automatically aligned resource of Wiktionary and Word-Net that has (i) a very high domain coverage of word senses and (ii) an enriched sense representation, including pronunciations, etymologies, translations, etc.


Moreover, word senses from natural sciences and information sciences are, in general, better represented by Wiktionary.

word senses related to chemistry, math, and IT are almost exclusively found in Wiktionary.


The instances inferred by the rules are ranked based on the scores assigned to the rules and the top instances are produced as final output.

our method infers new instances of particular target relations by applying the following type of recursive rules to large corpora.


"Basic" results restrict the idiom and WSD features to those of Hashimoto and Kawahara (2009); "extended" results include our extensions.

classifiers were trained on 90% of instances from all MWE-types in the corpus and tested on the remaining 10%.


The results show that our method is effective.

the method generates questions for 76.5% test queries.


V ctx (t) is the vector of context words of t, which is constructed using Baidu query logs.

words occurring within the same queries as t are extracted as t's context words.


We then conducted error analysis on the data set.

we sampled 100 incorrect questions produced with QtQG and RcQA, respectively.


Since a popular question tends to contains popular N-grams, the language model also measures the popularity of the question.

we use the same question set as that in Section 4.1 to train a 3-gram language model.


Answer as a knowledge repository and investigates techniques to mine knowledge from them for improving a sentence-based complex question answering (QA) system.

we present a question-type-specific method (QTSM) that studies at extracting question-type-dependent cue expressions from the social Q&A pairs in which question types are the same as the submitted question.


QTSM prec denotes QTSM based on the classifier optimizing performance prec@k. This table indicates that the complex QA performance can be clearly improved by exploiting social Q&A collection.

we observe that: 1) QTSM obtains the best performance; e.g., the F 3 improvements of QTSM prec over MTM and QSM qa in terms of N =10 are 5.8% and 6.0%, respectively.


This paper describes efforts of NLP researchers to create a system to aid the relief efforts during the 2011 East Japan Earthquake.

we created a system to mine information regarding the safety of people in the disaster-stricken area from Twitter, a massive yet highly unorganized information source.


Soon after the disaster struck, many NLP researchers, engineers, and students from all over Japan (including the authors), spontaneously created a working group called "ANPI NLP" 4 to try to organize this information into a form that would be useful.

we focused on mining and organizing information on Twitter regarding safety of individuals in the disaster-stricken area, as people are initially more concerned about the safety of their family or friends than anything else.


The project began with a single Twitter post soon after the earthquake hit, imploring NLP researchers and engineers to think about what they could do to help in the time of need.

the creation of resources and tools that could be used to process information about the earthquake was cited as one example.


A = a 1 a 2 ...a n , can be formally described as: where the approximation is based on the assumption that each abbreviation depends only on the corresponding word (we are not considering oneto-many mappings in this study), and a word is dependent on its previous (n − 1) words.

this probability is represented by a traditional n-gram LM.


There has been significant work in information extraction from text data for products.

the extraction of product attributes and user sentiments has received wide attention.


We evaluated our disambiguation method on two benchmark data sets.

we use the entity linking data from TAC-KBP track in 2009 (M-cNamee andDang, 2009) and the same track in 2010 (Ji et al., 2010).


The proposed method builds on a search engine.

we mine candidate related persons for a query person q using q's search results and the query logs containing q.


We employ a search engine SE to facilitate the extraction of related persons.

: • We directly extract related persons from q's context in its search results returned by SE (flow A); • Guided by distributional hypothesis (Harris, 1985), we mine q's related persons appearing in similar contexts (also collected from search results) with q (flow B); • In addition, we exploit the query logs of SE, and extract q's related persons that co-occur with q in the same queries (flow C).


We evaluated our method with a set of 200 query persons randomly selected from Baidu 1 query logs, and compare it with Renlifang 2 , a well known system developed for related person extraction.

experiment results show that our approach consistently outperforms Renlifang with a gap of 8%-13% in terms of averaged P recision@K. 8.28% (0.915 vs. 0.845), 12.9% (0.9 vs. 0.797), and 9.73% (0.846 vs. 0.771) improvement has been achieved at rank 1, 5, and 10 respectively.


Two native Chinese speakers were asked to rate all the candidates extracted by our approach as well as Renlifang results on a 4point scale, i.e., 0,1,2,3.

a potential related person p of a query person q gets the rating 0 if p and q are not related.


In detail, the upper part of Figure 2 shows the performances of two systems in terms of AvgP rec@K. As can be seen, our method (SVM-REG) consistently outperforms Renlifang (RLF) by 8%-13%.

the performance gaps between these two  Table 1: Example of top-10 related persons for query person "周润发".


Table 2 tabulates some statistics of candidate related persons extracted from three resources.

the first line of the table shows the number of test persons for which each resource can provide candidate related persons.


However, the qualities of the candidates differ a lot.

the resource based on contextual similarity provides 10 candidate related persons for all the 200 test persons, but the precision is below 65%, which is the lowest.